TrainImageContentDetector

TrainImageContentDetector[{img1{bbox1class1,},}]

trains a ContentDetectorFunction[] based on the examples given.

Details

Examples

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Basic Examples  (1)

Train a basic object detector:

Apply the detector on a new image:

Highlight the detection on the input image:

Options  (5)

PerformanceGoal  (1)

Use PerformanceGoal"Quality" to emphasize the quality of the result:

Use PerformanceGoal"Speed" to emphasize the speed of computation:

ProgressReporting  (1)

By default, progress is reported in a dynamic panel:

Use ProgressReportingFalse to avoid displaying the progress panel:

TargetDevice  (1)

Train a detector using the default system GPU, if available:

If a compatible GPU is not available, a message is issued:

TimeGoal  (1)

The training time can be influenced by several factors, such as the number of examples and classes:

Use TimeGoal to specify a target time for the training:

ValidationSet  (1)

By default, only cross-validation is performed on the detector:

Use ValidationSet to provide separate validation examples:

Wolfram Research (2021), TrainImageContentDetector, Wolfram Language function, https://reference.wolfram.com/language/ref/TrainImageContentDetector.html.

Text

Wolfram Research (2021), TrainImageContentDetector, Wolfram Language function, https://reference.wolfram.com/language/ref/TrainImageContentDetector.html.

CMS

Wolfram Language. 2021. "TrainImageContentDetector." Wolfram Language & System Documentation Center. Wolfram Research. https://reference.wolfram.com/language/ref/TrainImageContentDetector.html.

APA

Wolfram Language. (2021). TrainImageContentDetector. Wolfram Language & System Documentation Center. Retrieved from https://reference.wolfram.com/language/ref/TrainImageContentDetector.html

BibTeX

@misc{reference.wolfram_2022_trainimagecontentdetector, author="Wolfram Research", title="{TrainImageContentDetector}", year="2021", howpublished="\url{https://reference.wolfram.com/language/ref/TrainImageContentDetector.html}", note=[Accessed: 02-July-2022 ]}

BibLaTeX

@online{reference.wolfram_2022_trainimagecontentdetector, organization={Wolfram Research}, title={TrainImageContentDetector}, year={2021}, url={https://reference.wolfram.com/language/ref/TrainImageContentDetector.html}, note=[Accessed: 02-July-2022 ]}